Artificial Intelligence Journey Towards The Center Of The Enterprise
Fabrinet provides complex optical and electro-mechanical components, modules, and bulk optics. These components serve the markets of data communications, telecommunications, networking, medical devices, and automotive technologies.
Marc Andreessen had famously said software is eating the world. He probably had Artificial Intelligence (AI) in the back of his mind. In its simplistic form, AI enables a machine to perform human-like tasks, such as image, voice, and text recognition, natural language processing and understanding human-like perception. The journey of AI from Expert Systems in the early eighties to Heuristics analysis, machine learning, and finally to present day deep learning has been a roller coaster ride. Just a few years back, people thought neural networks were something academicians talked in their leisure time. This change, where AI is becoming more and more mainstream and affordable, has come about with the convergence of big data, availability of parallel processing advancements such as GPUs on public clouds and breakthroughs in machine learning.
Machine learning is set of algorithms that enable a computer program to recognize patterns in data sets and interpret those patterns to provide meaningful insights. Machine learning can be supervised or unsupervised. In the supervised learning you are training machine learning task for every input with corresponding target output. In supervised learning, machine is trained with labeled data and looks at data with specific parameters. Human input and bias are an ingredient of the supervised learning making it more expensive and limited in use.
In the unsupervised learning the information is classified without the help of trainers or instructors. The machine finds structure or relationships among different inputs. One example of unsupervised learning is clustering where new input data is automatically put into an appropriate cluster. The affordable processing power and storage coupled with explosion of Big Data from multitude of sources such as text, images, and connected devices is making it easier for machines to train and learn in the unsupervised mode.
Unsupervised learning is a precursor to deep learning where most of the benefits for an enterprise will be realized using AI. Deep learning systems can learn from iterative data computations. They just don't follow
Marc Andreessen had famously said software is eating the world. He probably had Artificial Intelligence (AI) in the back of his mind. In its simplistic form, AI enables a machine to perform human-like tasks, such as image, voice, and text recognition, natural language processing and understanding human-like perception. The journey of AI from Expert Systems in the early eighties to Heuristics analysis, machine learning, and finally to present day deep learning has been a roller coaster ride. Just a few years back, people thought neural networks were something academicians talked in their leisure time. This change, where AI is becoming more and more mainstream and affordable, has come about with the convergence of big data, availability of parallel processing advancements such as GPUs on public clouds and breakthroughs in machine learning.
Machine learning is set of algorithms that enable a computer program to recognize patterns in data sets and interpret those patterns to provide meaningful insights. Machine learning can be supervised or unsupervised. In the supervised learning you are training machine learning task for every input with corresponding target output. In supervised learning, machine is trained with labeled data and looks at data with specific parameters. Human input and bias are an ingredient of the supervised learning making it more expensive and limited in use.
In the unsupervised learning the information is classified without the help of trainers or instructors. The machine finds structure or relationships among different inputs. One example of unsupervised learning is clustering where new input data is automatically put into an appropriate cluster. The affordable processing power and storage coupled with explosion of Big Data from multitude of sources such as text, images, and connected devices is making it easier for machines to train and learn in the unsupervised mode.
Unsupervised learning is a precursor to deep learning where most of the benefits for an enterprise will be realized using AI. Deep learning systems can learn from iterative data computations. They just don't follow
